Dataset Information

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads.

ABSTRACT: Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

SUBMITTER: Keskus A

PROVIDER: S-EPMC10996739 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads.

Keskus Ayse A Bryant Asher A Ahmad Tanveer T Yoo Byunggil B Aganezov Sergey S Goretsky Anton A Donmez Ataberk A Lansdon Lisa A LA Rodriguez Isabel I Park Jimin J Liu Yuelin Y Cui Xiwen X Gardner Joshua J McNulty Brandy B Sacco Samuel S Shetty Jyoti J Zhao Yongmei Y Tran Bao B Narzisi Giuseppe G Helland Adrienne A Cook Daniel E DE Chang Pi-Chuan PC Kolesnikov Alexey A Carroll Andrew A Molloy Erin K EK Pushel Irina I Guest Erin E Pastinen Tomi T Shafin Kishwar K Miga Karen H KH Malikic Salem S Day Chi-Ping CP Robine Nicolas N Sahinalp Cenk C Dean Michael M Farooqi Midhat S MS Paten Benedict B Kolmogorov Mikhail M

medRxiv : the preprint server for health sciences 20240326

Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate ...[more]

PMID: 38585974

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Project description:MotivationAligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds.ResultsWe present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting.Availability and implementationGraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

Dataset Information

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads.

Publications

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads.

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